Chapter 1: What is the main topic discussed in this episode?
We were matching the perplexity, but we were able to be like 10 times faster. That was super exciting to me. And I really wanted to see what happens if you train something bigger than a GPT-2 model, possible to build something commercially viable. And that's why I started the company to scale things up.
The arithmetic intensity of inference workloads that we have today with an ultra aggressive model is very bad. The utilization is very low and that's why people are building massive data centers or even building custom chips, AI inference chips that are better suited for that kind of work. Basically, if you can generate more tokens per second,
What this means is that for the same amount of hardware, for the same number of GPUs, you can produce more tokens. And so the cost per token is going to go down. And that's why we're able to serve our models much more cheaply than what you would get because we make better use of the existing hardware. So now the Mercury models that we have in production are significantly larger.
They've been trained on more data. That's going to enable Mercury models to be even smarter. It's going to have much better planning and kind of like reasoning capabilities. And so that's going to enable a lot of agentic use cases that people really care about. They're going to make them really, really fast.
Welcome, humans, to the Neuron AI Podcast. I'm your host, Corey Knowles, and I'm joined, as always, by the man who could turn a GPU benchmark into a bedtime story, Grant Harvey. How's it going today, man?
It's going great.
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Chapter 2: What is diffusion and how does it differ from traditional models?
Don't put me on the spot to do that right this moment, though. I'd have to think of some mechanics there.
Oh, well, here in just a few, we're going to be joined by Stefano Orman, Stanford University computer science professor and the founder of Inception Labs that created the Mercury Diffusion Large Language Models. But first, Brant's going to share us a little context before we get in there.
Yeah, so image diffusion models work in an entirely different way than the next token predicting GPT models. So we've invited Stefano today because he's taken that same technology and applied it to LLMs. And it has the potential to transform how AI is used in all types of settings from agents to complex enterprise workflows.
Excellent. Well, before we bring him on, I want to take just a quick second to show you Mercury in action because I think seeing it really matters and will keep you really interested. You'll understand why you need to be watching this video. So what you see here on my screen, this is the Inception Lab site. And if you go up top, you can go to Mercury Chat.
And down here, I'm just going to grab one of their suggested prompts. And I love this one. Simulate a roundtable discussion between Einstein, Ada Lovelace, and Alan Turing. Now you want to make sure you click this diffusion button because the diffusion button gives you the visual of how cool this is. So watch this. All right. Watch how this works.
Whoa.
And if you go down.
That is so much cooler than the typewriter effect of the AI.
Is that not insane?
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Chapter 3: How does diffusion impact the efficiency of language models?
So diffusion is a type of generative AI model. It's the kind of model that is commonly used to generate images, video, music. And you're probably familiar with the, you know, ChatGPTs or Geminis or Cloud, where you kind of like see the models generate text, kind of like left to right, one token at a time.
A diffusion model works very differently in the sense that it generates the full object from the beginning, and then it refines it by kind of like fixing mistakes, making it sharper, making it look better and better.
And it's a very different kind of like solution that it's more parallel in the sense that the neural network is able to modify many components of the image or the text at the same time. And that's why diffusion models tend to be a lot faster than traditional autoregressive models that kind of like work left to right one token at a time.
Okay. Right.
And how is it that they're actually reasoning over the original version that they create? How do they know that the first version isn't good?
Yeah. That's a great question. Yeah, that's a great question. And it really stems from the way the models are trained. A traditional autoregressive model, like a GPT model, is trained to, there is a neural network and it's trained to predict the next token, the next word. And that's how you use it at inference time.
You give it a question and then it will try to predict the answer left to right, one token at a time. The fusion language model, it's trained to remove mistakes, fix mistakes. So you kind of like start with clean text or clean code. You artificially add mistakes and then you train the model to fix those mistakes. And that's how the model is also used at inference time.
You start with kind of like a full answer and then you refine it. And so it's a very different way of training the models. It's a very different way of using the models at the inference time.
And it happens at the speed of lightning.
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Chapter 4: What are the current applications of Mercury in various industries?
But basically what we did is we took a GPT-2 sized model and we train it as a diffusion model and as an autoregressive model on the same data. And so what we found was that the quality was the same.
So you were able to actually, you know, if you think about perplexity, which is the way people usually, um, you know, used to, to figure out how good the model fits the data, what's the quality of the generations you get. We were matching the perplexity, but we were able to be like 10 times faster. And so that was super exciting to me.
And I really wanted to see what happens if you train something bigger than a GPT-2 model. you know, is it possible to build something commercially viable? And that's why I started the company to scale things up.
And you've been scaling up from GPT-2 caliber since then, right?
Since then, yes, yes. Now the Mercury models that we have in production are significantly larger. They've been trained on more data. There is a lot of engineering work that went into kind of like post-training the models and making sure that they would be useful for tasks that people care about, like commercial kind of use cases of LLMs.
Are they kind of similar in size? Should we be thinking about them the same way?
Oh, like parameters and such?
How do we compare them to a traditional language model?
Yeah, so the models are still fairly large in terms of the number of parameters. We're still using actually similar architectures. So under the hood, it's still a transformer.
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